Inventory, model risk management, audit-of-record, BAA chain, and workforce governance — built into the platform, not bolted on as policy.
Regulators in banking, healthcare, government, legal, and insurance have converged on the same expectation for AI: treat it like the regulated technology it is. Inventory every model. Tier it by risk. Validate before deployment. Monitor in production. Capture audit evidence. Bind every interaction to identity.
Most institutions try to satisfy this with policy documents and vendor SOC 2 reports. The institutions that pass the second examination get there with architecture — a platform that captures audit evidence by default, binds identity to every prompt, and routes per workload to keep sensitive data inside the perimeter.
ibl.ai is the platform layer that turns AI governance from a policy document into an operational reality across every regulated industry the institution serves.
Regulated industries face the same governance failure mode: AI gets deployed in pilots, the pilots succeed, scaling begins, and the governance posture has not caught up. The first examination — OCC, HHS OCR, state attorney general, accreditor, internal audit — reveals the gap.
The gap is rarely a missing policy. It is missing evidence. The policy says audit logs are maintained. The vendor dashboard cannot produce them in the auditor's format. The policy says identity is bound to every AI session. The pilot used personal accounts because the IdP integration was deferred. The policy says model risk is managed. The model inventory is a spreadsheet that has not been updated since the pilot launched.
The gap closes when the architecture supports the policy by default — not when the policy is rewritten harder.
Most institutions undercount AI by 40-60% because shadow usage and embedded AI in SaaS tools are not inventoried. Examiners discover the gap quickly.
Failed model-risk examinations, surprise findings that block scaling, and the cost of building inventory under audit pressure rather than as a planned project.Vendor dashboards capture inference events in vendor formats, on vendor retention schedules, accessible only through vendor interfaces — not the institution's audit-of-record system.
Examiners request 90-day evidence in the institution's format and the vendor dashboard cannot produce it; the institution improvises under pressure.AI sessions started through personal accounts, shared service accounts, or unbound API keys break the identity chain that downstream compliance evidence depends on.
When an AI-assisted decision is challenged in litigation, an examination, or an incident response, the institution cannot defensibly attribute the prompt to the workforce member.SR 11-7 in banking, clinical-governance in healthcare, and model-risk in insurance were built for quantitative models. AI/LLM models are inside scope but have not been brought into the institutional MRM program.
AI deployments operate outside the institution's existing model-risk discipline, creating a parallel governance regime that examiners flag and the institution scrambles to unify.Sanctioned tools are one workflow. Personal accounts on unsanctioned AI tools handle the rest. The institution's audit chain captures none of it.
Shadow usage drives the majority of AI-related data-exposure incidents in regulated industries, and the institution discovers the exposure only after the incident is public.Every model and agent deployed on the platform appears in the inventory automatically — including the third-party LLMs they call. The inventory is queryable, exportable, and aligned with SR 11-7, clinical-governance, and accreditor frameworks.
Risk tiers — high (customer- or patient-facing, fiduciary, advisory), medium (operations), low (productivity) — are configured at the platform level and applied to every model and agent. Tier-appropriate validation requirements are enforced before deployment.
Audit-of-record events flow into the institution's SIEM — Splunk, Sentinel, Elastic, Sumo Logic, Datadog — in the institution's format, on the institution's retention schedule. Vendor dashboards are supplementary; the SIEM is the source of truth.
Every AI session is identity-bound through the institution's identity provider (Okta, Azure AD, Ping, Shibboleth) via SAML 2.0, OIDC, or SCIM. No personal accounts, no shared service accounts, no unbound API keys for institutional work.
PHI, customer data, classified information, or other regulated data routes to a local open-weights model running on institutional GPUs. Non-sensitive workloads route to BAA- or contract-covered frontier models. The platform captures evidence for every path.
Sanctioned-path adoption, training completion, attestation, and DLP integration for shadow-usage detection are platform-level workflows. Compliance, security, and learning teams see the same data, in their existing systems.
Every model, agent, and third-party LLM the platform calls appears in the inventory automatically. Queryable by risk tier, business owner, validator, and deployment status. Exportable for SR 11-7, clinical-governance, and accreditor evidence.
Risk tiers configured at platform level. Tier-appropriate validation, monitoring, and change-management requirements applied automatically. New deployments above tier threshold trigger pre-deployment review workflow.
Every prompt, response, and model invocation captured as a structured event with user identity, timestamp, model identifier, prompt reference, and response reference. Streamed to the institution's SIEM in real time.
Every AI session bound to a named workforce member through the institution's IdP. SAML 2.0, OIDC, SCIM. No personal accounts; no shared service accounts; no unbound API keys for production work.
Routing decisions reflect the BAA and contract coverage of each external service. PHI workloads route to local models or BAA-covered frontier paths. Audit evidence captures which path each prompt took.
Sanctioned-path enforcement, training completion tracking, attestation workflows, and DLP integration for shadow-usage detection. Compliance, security, and learning teams see unified data.
The complete platform code is the institution's, under a perpetual license. Vendor changes, model deprecations, and price moves do not stall regulated AI deployments — they become routing changes inside the institution's platform.
| Aspect | Without | With ibl.ai |
|---|---|---|
| Model and Agent Inventory | Spreadsheet inventory updated manually, missing shadow usage and embedded SaaS AI. Quickly out of date and not defensible in examination. | Automatic inventory of every model and agent deployed on the platform, including third-party LLMs called. Queryable, exportable, and continuously current. |
| Risk Tiering | Risk tiers defined in policy but applied inconsistently across pilot deployments and production rollouts. | Risk tiers configured at platform level and enforced for every deployment. Tier-appropriate validation and monitoring applied automatically. |
| Audit-of-Record | Vendor dashboards in vendor formats, on vendor retention schedules, accessible through vendor interfaces. Not the institution's source of truth. | Every prompt and response captured in the institution's SIEM in the institution's format, on the institution's retention schedule. Source of truth is institutional. |
| Identity Binding | Personal accounts, shared service accounts, and unbound API keys in production. Identity chain breaks under examination. | Every AI session bound to a named workforce member through the institutional IdP. Identity chain is unbroken end-to-end. |
| BAA and Contract Coverage | BAAs and vendor contracts in folders, not actively managed against actual workload routing. Scope drift goes unnoticed. | BAA and contract metadata attached to each routing destination. Every routing decision captured in audit evidence. |
| Workforce Governance | Sanctioned-path adoption depends on vendor UX; shadow usage invisible to the institution. | Sanctioned-path enforcement at the platform level, DLP integration for shadow detection, training and attestation tracked in compliance systems. |
| Continuity | Vendor changes, deprecations, and price moves stall regulated AI deployments and trigger re-procurement. | Source-code ownership and per-workload routing make vendor changes routing changes inside the institutional platform. |
Spreadsheet inventory updated manually, missing shadow usage and embedded SaaS AI. Quickly out of date and not defensible in examination.
Automatic inventory of every model and agent deployed on the platform, including third-party LLMs called. Queryable, exportable, and continuously current.
Risk tiers defined in policy but applied inconsistently across pilot deployments and production rollouts.
Risk tiers configured at platform level and enforced for every deployment. Tier-appropriate validation and monitoring applied automatically.
Vendor dashboards in vendor formats, on vendor retention schedules, accessible through vendor interfaces. Not the institution's source of truth.
Every prompt and response captured in the institution's SIEM in the institution's format, on the institution's retention schedule. Source of truth is institutional.
Personal accounts, shared service accounts, and unbound API keys in production. Identity chain breaks under examination.
Every AI session bound to a named workforce member through the institutional IdP. Identity chain is unbroken end-to-end.
BAAs and vendor contracts in folders, not actively managed against actual workload routing. Scope drift goes unnoticed.
BAA and contract metadata attached to each routing destination. Every routing decision captured in audit evidence.
Sanctioned-path adoption depends on vendor UX; shadow usage invisible to the institution.
Sanctioned-path enforcement at the platform level, DLP integration for shadow detection, training and attestation tracked in compliance systems.
Vendor changes, deprecations, and price moves stall regulated AI deployments and trigger re-procurement.
Source-code ownership and per-workload routing make vendor changes routing changes inside the institutional platform.
AI deployments operate under the institution's existing model-risk discipline rather than as a parallel regime; examination preparation moves from quarters to weeks.
PHI-heavy workflows stay inside the covered entity, BAA evidence is unified across the institution's audit-of-record system, and clinical-governance committees have the data they need for review.
The platform's air-gapped topology and source-code ownership satisfy the strictest residency, segmentation, and supply-chain commitments federal and state regulators require.
Client data and privileged work product stay inside the firm; audit evidence supports discovery responses involving AI-generated analysis.
AI underwriting and claims decisions are documented to a standard that supports state regulator inquiries, actuarial review, and consumer-protection challenges.
Student data stays inside the institution, FERPA evidence is unified, and the governance posture supports accreditor expectations on responsible AI use in education.
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